Algorithm Zoo · Machine learning
Quantum Generative Adversarial Networks
Also known as: QGAN, Quantum generative models
First described: Dallaire-Demers, Killoran; Lloyd, Weedbrook, 2018
The problem
Train a parameterized quantum circuit to generate samples from a target distribution.
Adversarial training: a quantum generator G(θ) produces |ψ(θ)⟩, samples z ∼ ⟨z|ψ(θ)⟩|². A discriminator (classical or quantum) tries to distinguish real samples from G's samples. θ is updated by gradient descent (parameter-shift rule).
Best classical
Diffusion models, normalizing flows, classical GANs — extremely strong on real data.
Quantum complexity
O(L·N) gates per generator call for L-layer N-qubit ansatz; many calls per training step.
Our verdict
Research vehicle, not a generative-model competitor. The classical baseline keeps getting stronger faster than the quantum hardware. Best near-term use is as a distribution-loading primitive for downstream quantum algorithms — and even there, classical methods often suffice.
When to use it
- Research into expressibility of parameterized circuits as generative models.
- Hybrid pipelines where the quantum sampler proposes for a classical sampler (Bornholdt 2024 work).
When not to use it
- Image generation, audio, anything where classical generative models exist.
- Production sampling — diffusion models are extraordinarily strong and the gap is widening.
Classical baseline
Diffusion models (Ho et al. 2020) and consistency models (Song et al. 2023) dominate image generation. For tabular data, normalizing flows and copula-based methods are highly tuned. No QGAN result has matched a strong classical baseline on natural data.
Hardware cost
Each training step requires O(L·N) gates per shot, ~10^3 shots per gradient estimate, ~10^3 steps to converge. NISQ noise compounds across this.
Key papers
- Quantum generative adversarial learning ↗
Lloyd, Weedbrook · 2018 · Phys. Rev. Lett.
- Quantum generative adversarial networks for learning and loading random distributions ↗
Zoufal, Lucchi, Woerner · 2019 · npj Quantum Information
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Last verified: 2026-05-24